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Databricks Certified-Data-Engineer-Professional Exam Syllabus Topics:

Section	Weight	Objectives
Security and Governance	~10%	- Manage Unity Catalog permissions and ACLs - Implement row-level security, column masking, and compliance
Data Modeling	~10%	- Design scalable Delta Lake schemas and clustering - Apply dimensional modeling techniques
Monitoring, Logging, and Troubleshooting	~8%	- Diagnose common pipeline and job failures - Use Spark UI, Query Profiler, and system tables
Developing Code for Data Processing using Python and SQL	~22%	- Manage dependencies, libraries, and UDFs - Implement scalable Python/SQL code and project structures - Build pipelines with Lakeflow Spark Declarative Pipelines and Auto Loader
Data Sharing and Federation	~8%	- Configure Delta Sharing and Lakehouse Federation
Streaming Workloads and Change Data Capture	~11%	- Implement reliable streaming pipelines - Apply AUTO CDC APIs and exactly-once semantics
CI/CD, Testing, and Deployment	~6%	- Deploy with Declarative Automation Bundles, CLI, and REST API - Implement testing and deployment pipelines
Data Transformation, Cleansing, and Quality	~12%	- Apply advanced Spark transformations - Enforce data quality and quarantine bad data
Cost and Performance Optimization	~13%	- Optimize queries, clusters, and storage - Leverage system tables and observability tools

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Databricks Certified Data Engineer Professional Sample Questions (Q106-Q111):

NEW QUESTION # 106
How are the operational aspects of Lakeflow Declarative Pipelines different from Spark Structured Streaming?

A. Lakeflow Declarative Pipelines can write to Delta Lake format, while Structured Streaming cannot.
B. Lakeflow Declarative Pipelines manage the orchestration of multi-stage pipelines automatically, while Structured Streaming requires external orchestration for complex dependencies.
C. Lakeflow Declarative Pipelines automatically handle schema evolution, while Structured Streaming always requires manual schema management.
D. Structured Streaming can process continuous data streams, while Lakeflow Declarative Pipelines cannot.

Answer: B

Explanation:
Databricks documentation explains that Lakeflow Declarative Pipelines build upon Structured Streaming but add higher-level orchestration and automation capabilities. They automatically manage dependencies, materialization, and recovery across multi-stage data flows without requiring external orchestration tools such as Airflow or Azure Data Factory. In contrast, Structured Streaming operates at a lower level, where developers must manually handle orchestration, retries, and dependencies between streaming jobs. Both support Delta Lake outputs and schema evolution; however, Lakeflow Declarative Pipelines simplify management by declaratively defining transformations and data quality expectations. Hence, the correct distinction is A -- automated orchestration and management in Lakeflow Declarative Pipelines.

NEW QUESTION # 107
Assuming that the Databricks CLI has been installed and configured correctly, which Databricks CLI command can be used to upload a custom Python Wheel to object storage mounted with the DBFS for use with a production job?

A. configure
B. jobs
C. workspace
D. fs
E. libraries

Answer: D

Explanation:
https://docs.databricks.com/en/archive/dev-tools/cli/dbfs-cli.html

NEW QUESTION # 108
A data company uses Databricks Unity Catalog and has multiple enterprise data sources, including PostgreSQL, Snowflake, and SQL Server. The central data platform team wants to configure Lakehouse Federation so analysts can query external tables directly in Databricks using Databricks SQL, without duplicating data. Which steps are necessary to configure Lakehouse Federation in a secure and governed manner?

A. Configure connections and foreign catalog in Unity Catalog, then grant access to foreign catalogs, schemas, and tables using Unity Catalog permissions.
B. Use Partner Connect to create linked datasets, and apply table ACLs at the source system to govern access through Databricks.
C. Mirror the external datasets into Delta Lake using Auto Loader, and govern them using Data Lineage and System Tables.
D. Create external locations and storage credentials to connect to each database, then register foreign tables in Unity Catalog.

Answer: A

Explanation:
Lakehouse Federation is configured by defining secure connections to external data sources and registering them as foreign catalogs in Unity Catalog. Access is then governed using Unity Catalog permissions at the catalog, schema, and table levels, enabling analysts to query external tables securely without data duplication.

NEW QUESTION # 109
A data engineer is creating a daily reporting job. There are two reporting notebooks--one for weekdays and one for weekends. An "if/else condition" task is configured as
{{job.start_time.is_weekday}} == true to route the job to either the weekday or weekend notebook tasks. The same job would be used across multiple time zones. Which action should a senior data engineer take upon reviewing the job to merge or reject the pull request?

A. Reject, as the {{job.start_time.is_weekday}} is not a valid value reference.
B. Merge, as the job configuration looks good.
C. Reject, as they should use {{job.trigger_time.is_weekday}} instead.
D. Reject, as the {{job.start_time.is_weekday}} is for the UTC timezone.

Answer: D

Explanation:
Databricks parameter templates like {{job.start_time.is_weekday}} evaluate in UTC time by default, not in local workspace or regional time zones. Therefore, when jobs are configured to run across different time zones, relying on is_weekday using UTC may cause scheduling and task routing mismatches (for example, triggering the weekday notebook in one region while it's still the weekend locally).
Databricks recommends adjusting conditional logic or pipeline parameters explicitly to handle time zone conversions if business requirements depend on local times. Because the engineer's configuration does not account for this behavior, a senior data engineer should reject the pull request and suggest time-zone-aware logic before merging.

NEW QUESTION # 110
A platform team is creating a standardized template for Databricks Asset Bundles to support CI/CD. The template must specify defaults for artifacts, workspace root paths, and a run identity, while allowing a "dev" target to be the default and override specific paths. How should the team use databricks.yml to satisfy these requirements?

A. Use roots, modules, profiles, actor, and targets; where profiles contain workspace and artifacts defaults and actor sets run identity.
B. Use project, packages, environment, identity, and stages; set dev as default stage and override workspace under environment.
C. Use bundle, artifacts, workspace, run_as, and targets at the top level; set one target with default:true and override workspace paths or artifacts under that target.
D. Use deployment, builds, context, identity, and environments; set dev as default environment and override paths under builds.

Answer: C

Explanation:
In Databricks Asset Bundles, the databricks.yml file defines all top-level configuration keys, including bundle, artifacts, workspace, run_as, and targets. The targets section defines specific deployment contexts (for example, dev, test, prod). Setting default: true for a target marks it as the default environment. Overrides for workspace paths and artifact configurations can be defined inside each target while keeping defaults at the top level.

NEW QUESTION # 111
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